Evolving Multiple Agents by Genetic Programming

author = "Hitoshi Iba",

title = "Evolving Multiple Agents by Genetic Programming",

booktitle = "Advances in Genetic Programming 3",

publisher = "MIT Press",

year = "1999",

editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline",

chapter = "19",

pages = "447--466",

address = "Cambridge, MA, USA",

month = jun,

keywords = "genetic algorithms, genetic programming, QGP",

ISBN = "0-262-19423-6",

URL = "

http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch19.pdf",

DOI = "

doi:10.7551/mitpress/1110.003.0024",

abstract = "On the emergence of the cooperative behaviour for multiple agents by means of Genetic Programming (GP). Our experimental domains are multi-agent test beds, i.e., the robot navigation task and the Tile World. The world consists of a simulated robot agent and a simulated environment which is both dynamic and unpredictable. In our previous paper, we proposed three types of strategies, i.e, homogeneous breeding, heterogeneous breeding, and co-evolutionary breeding, for the purpose of evolving the cooperative behavior. We use the heterogeneous breeding in this paper. The previous Q-learning approach commonly used for the multi-agent task has the difficulty with the combinatorial explosion for many agents. This is because the state space for Q-table is so huge for the practical computer resources. We show how successfully GP-based multi-agent learning is applied to multi-agent tasks and compare the performance with Q-learning by experiments. Thereafter, we conduct experiments with the evolution of the communicating agents. The communication is an essential factor for the emergence of cooperation. This is because a collaborative agent must be able to handle situations in which conflicts arise and must be capable of negotiating with other agents to reach an agreement. The effectiveness of the emergent communication is empirically shown in terms of the robustness of generated GP programs.",

notes = "AiGP3 See http://cognet.mit.edu",